Gender Prediction From Tweets: Improving Neural Representations With Hand-Crafted Features
| dc.contributor.author | Tekir, Selma | |
| dc.contributor.author | Sezerer, Erhan | |
| dc.contributor.author | Polatbilek, Ozan | |
| dc.contributor.other | 03.04. Department of Computer Engineering | |
| dc.contributor.other | 03. Faculty of Engineering | |
| dc.contributor.other | 01. Izmir Institute of Technology | |
| dc.date.accessioned | 2019-09-02T13:21:00Z | |
| dc.date.available | 2019-09-02T13:21:00Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn ’where to look’. This model1 is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the proposed model (RNNwA + n-gram) achieves state-of-the-art performance on English and has competitive results on Spanish and Arabic. | en_US |
| dc.identifier.citation | Tekir, S., Sezerer, E., Polatbilek, O. (2019). Gender prediction from tweets: Improving neural representations with hand-crafted features. Yayın için başvurusu yapılmış metin. | en_US |
| dc.identifier.doi | 10.48550/arXiv.1908.09919 | |
| dc.identifier.uri | https://hdl.handle.net/11147/7251 | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.1908.09919 | |
| dc.language.iso | en | en_US |
| dc.publisher | Cornell University | en_US |
| dc.relation.ispartof | arXiv | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/us/ | * |
| dc.subject | RNN Model | en_US |
| dc.subject | Datasets | en_US |
| dc.subject | Model architecture | en_US |
| dc.subject | Neural network-based models | en_US |
| dc.subject | Neural representations | en_US |
| dc.title | Gender Prediction From Tweets: Improving Neural Representations With Hand-Crafted Features | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.id | 0000-0002-0488-9682 | |
| gdc.author.institutional | Tekir, Selma | |
| gdc.author.institutional | Sezerer, Erhan | |
| gdc.author.institutional | Tekir, Selma | |
| gdc.coar.access | open access | |
| gdc.coar.type | text::journal::journal article | |
| gdc.description.department | İzmir Institute of Technology. Computer Engineering | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W2970090256 | |
| gdc.openalex.normalizedpercentile | 0.41 | |
| gdc.opencitations.count | 0 | |
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